Blessay: A Taxonomy for Five levels of Intent Based Networking

In my view, Intent Based Networking (IBN) is going to be journey. Its not a specific thing like ‘routing’ or ‘traffic shaping’. Its not a product or a feature, its group of technologies. In the same way that Software Defined Networking is a broad definition.

I think I can see a way to classify how IBN is going to evolve. And defining an evolution means you need a taxonomy to judge how you are progressing along the path.

Here is a taxonomy for five levels of Intent Based Networking (loosely based on the five levels of automation in vehicles):

Manual Operations

Automation

Visibility

Data Analysis and Awareness

Reactive and Self-Aware

Autonomous and Self-Aware.

Lets go through them.

0. Manual Operations – Skilled Operators are responsible for all activities. They translate the business intent into network operation through configuration changes, network knowledge and simple monitoring over legacy APIs. Not really a level but this is where most people are today.

1. Automation – replace manual tasks with robots (scripts) and improve the quality the manual operation.
Short term value derived from accuracy, repeatability and speed of operation. Automation creates new problem to solve – visibility. You cannot scale automation without viability to measure, validate and inspect the result of those changes. This drives the next level.
Common Mistake: Intent is translated into action via the programmed tasks. This programming is faster and more accurate which creates a sense of dynamism and purpose but fundamentally you haven’t changed the nature of ownership or change. Automation will improve existing process/work but it won’t transform or re-engineer it.

2. Visibility – tools and applications that provide visibility into the running status of the network including the changing, variable, temporal state.

Core Technologies: Sub-second accuracy of temporal and transient states of hardware and software are key elements. Constant streaming of the data from all network devices to a central software application for collection. Management and sustenance of data lakes is key. Important elements are data repository, learning engine and graphical interface that presents the data to the engineer.
Technologies like formal verification, streaming telemetry, data models, APIs, protocols such as gRPC are creating data lakes of information about the fixed and temporal state of the entire network. Risk of this level of visibility is information overload where a human operator will be paralysed by data and unable to correctly.

Common Mistake: Having network visibility can create a false of sense of control or awareness. A network changes over short and long intervals. It also requires substantial effort to sustain a monitoring system although automation can assist. A monitoring system should not be an ‘add-on’ to the network but a critical element.

3. Data Analysis and Realisation – to extract meaning from the available information. Configuration, architecture, flow levels, path changes, device issues are just of the some of analysis that needs to occur. This analysis must then be “realised” into something human meaningful. Existing technologies will be adapted into networking use cases, modelled into standard modes by large scale cloud ML operations and then delivered to a local network controller. The local network controller will attempt to map the network datum stream onto the use cases to provide ‘intelligent’ assessment.

The models, likely provided by vendors, are key to success of this analysis. The business model here is attractive

monitors device, path and performance to make reactive changes and adapts the network within defined boundaries.

All functions are defined by a software platform that can modify the network services offered. This includes operation of NFV instances for security, DOS, path selection, identity,

This combines the previous two stages of the visibility and realisation to modify the network configuration. Recognition of repeated patterns, outages, known failure conditions, trends and events are beyond what a human can achieve (thus super human) and the ability to react & adapt through change on device configuration, path management, service chaining. This will result in self-operated load shifting, path activation and remove, multi-party traffic shifting.

5. Autonomous – the network becomes self-training and self-operating. The logic engines behind the modelling have the expertise to become self-operating networks. Day-to-day operations are performed by the Intent Systems while Network Engineers spend time reviewing the process, deployment and design of the network. The day is consumed in research and design activities that directly add value to the business.

The EtherealMind View

Networking changes slowly. As a federated system, that is the nature of a networked system. I think that we can predict the paths of change from the position that we are in today.

What do you think ?

About Greg Ferro

Human Infrastructure for Data Networks. 25 year survivor of Corporate IT in many verticals, tens of employers working on a wide range of networking solutions and products.

Host of the Packet Pushers Podcast on data networking at http://packetpushers.net- now the largest networking podcast on the Internet.